Physics-informed model-based generative neural network for synthesizing scanner- and algorithm-specific low-dose CT exams

Accurate low-dose CT simulation is required to efficiently assess reconstruction and dose reduction techniques. Projection domain noise insertion requires proprietary information from manufacturers. Analytic image domain noise insertion methods are successful for linear reconstruction algorithms, however extending them to non-linear algorithms remains challenging. Emerging, deep-learning-based image domain noise insertion methods have potential, but few approaches have explicitly incorporated physics information and a texture-synthesis model to guide the generation of locally and globally correlated noise texture.

We proposed a physics-informed model-based generative neural network for simulating scanner- and algorithm-specific low-dose CT exams (PALETTE). It is expected to provide an alternative to projection domain noise insertion methods in the absence of manufacturers' proprietary information and tools.

PALETTE integrated a physics-based noise prior generation process, a Noise2Noisier sub-network, and a noise texture synthesis sub-network. The Noise2Noisier sub-network provided a bias prior, which, combined with the noise prior, served as the inputs to noise texture synthesis sub-network. Explicit regularizations in spatial and frequency domains were developed to account for noise spatial correlation and frequency characteristics. For proof-of-concept, PALETTE was trained and validated for a commercial iterative reconstruction algorithm (SAFIRE, Siemens Healthineers), using the paired routine and 25% dose images from CT phantoms (lateral size 30-40 cm; three training and four testing phantoms) and open-access patient cases (10 training and 20 testing cases). In phantom validation, noise power spectra (NPS) were compared in water background and tissue-mimicking inserts, using peak frequency and mean-absolute-error (MAE). In patient case evaluation, visual inspection and quantitative assessment were conducted on axial, coronal, and sagittal planes. Local and global noise texture were visually inspected in low-dose CT images and the difference images between routine and low dose. Noise levels in liver and fat were measured. Local and global 2D Fourier magnitude spectra of the difference images and the corresponding radial mean profiles were used to assess similarity in noise frequency components within tissues and entire field-of-view, using spectral correlation mapper (SCM) and spectral angle mapper (SAM). Several baseline neural network models (e.g., GAN) were included in the evaluation. Statistical significance was tested using a t-test for related samples.

PALETTE-derived NPS showed accurate noise peak frequency (PALETTE/reference: water 1.40/1.40 lp/cm; inserts 1.7/1.7lp/cm) and small MAE (≤0.65 HU²cm²). PALETTE created anatomy-dependent noise texture, showing realistic local and global granularity and streaks. No statistically significant difference was observed in noise levels (p > 0.05). Noise range was comparable across 3D image volume (PALETTE / reference):liver - [18.0, 53.4] / [19.3, 50.0] HU; fat - [11.7, 42.4] / [12.1, 41.3] HU. Percent absolute difference of local noise was small (mean ± standard deviation): liver 4.1%±3.1%, fat 4.6%±3.1%. Noise frequency distribution was close to the reference (mean per case): SCM ≥ 0.92, SAM ≤ 0.22. Additionally, PALETTE outperformed all baseline models in visual inspection and quantitative comparison.

PALETTE can provide high-quality image domain noise insertion for simulating accurate low-dose CT images created with a commercial non-linear reconstruction algorithm.